Intelligent switching adaptive control for uncertain nonlinear dynamical systems

• Published in: Applied soft computing 2015-09, Vol.34, p.638-654
• Main Authors: Li, I-Hsum, Lee, Lian-Wang, Chiang, Hsin-Han, Chen, Pin-Cheng
• Format: Article
• Language: English
• Subjects: Adaptive control; Hopfield dynamic neural network; Switching control
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.04.057

(a) Structure of the Hopfield dynamic neural network (HDNN); (b) structure of the simple HDNN. •A switching adaptive control scheme using a Hopfield-based dynamic neural network (SACHNN) for nonlinear systems with external disturbances is proposed.•The IAC's limitation of gˆ(x)>ε can be solved by simply switching the IAC to the DAC, where ɛ is a positive desired value.•The Hopfield dynamic neural network (HDNN) is used to not only design DAC but also approximate the unknown plant nonlinearities in IAC design. In this paper, we aim at proposing a switching adaptive control scheme using a Hopfield-based dynamic neural network (SACHNN) for nonlinear systems with external disturbances. In our proposed scheme, an auxiliary direct adaptive controller (DAC) ensures the system stability when the indirect adaptive controller (IAC) is failed; that is, gˆ(x) approaches to zero, where gˆ(x) is the denominator of an indirect adaptive control law. The IAC's limitation of gˆ(x)>ε then can be solved by simply switching the IAC to the DAC, where ɛ is a positive desired value. The Hopfield dynamic neural network (HDNN) is used to not only design DAC but also approximate the unknown plant nonlinearities in IAC design. The designed simple structure of HDNN keeps the tracking performance well and also makes the practical implementation much easier because of the use of less and fixed number of neurons.